Graph Representation Learning via Ladder Gamma Variational Autoencoders

Abstract

We present a probabilistic framework for community discovery and link prediction for graph-structured data, based on a novel, gamma ladder variational autoencoder (VAE) architecture. We model each node in the graph via a deep hierarchy of gamma-distributed embeddings, and define each link probability via a nonlinear function of the bottom-most layer's embeddings of its associated nodes. In addition to leveraging the representational power of multiple layers of stochastic variables via the ladder VAE architecture, our framework offers the following benefits: (1) Unlike existing ladder VAE architectures based on real-valued latent variables, the gamma-distributed latent variables naturally result in non-negativity and sparsity of the learned embeddings, and facilitate their direct interpretation as membership of nodes into (possibly multiple) communities/topics; (2) A novel recognition model for our gamma ladder VAE architecture allows fast inference of node embeddings; and (3) The framework also extends naturally to incorporate node side information (features and/or labels). Our framework is also fairly modular and can leverage a wide variety of graph neural networks as the VAE encoder. We report both quantitative and qualitative results on several benchmark datasets and compare our model with several state-of-the-art methods.